Fivetran Case Studies Vida Health's Transformation: Personalized Healthcare through Modern Data Stack

Edit This Case Study Record

	Vida Health's Transformation: Personalized Healthcare through Modern Data Stack Fivetran

Vida Health's Transformation: Personalized Healthcare through Modern Data Stack

Fivetran

Technology Category	Analytics & Modeling - Big Data Analytics Functional Applications - Enterprise Resource Planning Systems (ERP)
Applicable Industries	Healthcare & Hospitals Oil & Gas
Applicable Functions	Product Research & Development Quality Assurance
Use Cases	Experimentation Automation Leasing Finance Automation
Services	Cloud Planning, Design & Implementation Services Testing & Certification
Challenge	Vida Health, a digital health company, was facing challenges with its data infrastructure. The company collects data on customers' medical history, past insurance claims, lab test results, and log data from health-tech devices to provide personalized virtual care. However, their custom-built solution using Python scripts and cron jobs to load and transform data in BigQuery was not scalable and often failed when data volume spiked. The pipeline was poorly documented and understood by only a few people on the data team, leading to reporting downtime of 2-3 days when issues arose. The company had recently consolidated its data engineering, data science, and data analytics functions into one team, aiming to improve collaboration. However, the existing data infrastructure was not reliable or accessible enough to best serve their customers and meet their goal of onboarding more than ten new clients in less than six months. Read More
About Customer	Vida Health is a San Francisco-based digital health company founded in 2014. The company provides personalized virtual care to individuals with chronic conditions such as diabetes, obesity, and depression through employer-sponsored plans. To tailor support and resources to individual needs, Vida Health collects data on the customer's medical history, past insurance claims, lab test results, and log data from health-tech devices such as fitness trackers and digital scales. The data enables Vida Health to monitor user progress, adjust treatment plans as needed, and give users visibility into results via the Vida Health mobile app. The company has a team size of 400 and uses a data stack comprising dbt Cloud, Fivetran Enterprise, Google Cloud, BigQuery, and Looker. Read More
Solution	Vida Health implemented Fivetran and dbt Cloud to transform their data infrastructure. Fivetran was used to centralize data from all their SaaS applications and proprietary Protected Health Information (PHI) from multiple sources into one source-of-truth warehouse. This provided built-in automation and security, especially crucial when handling health data. The company now uses Fivetran to centralize data from 148 different connectors into one BigQuery. Once the data was loaded into BigQuery with Fivetran, the team used dbt Cloud to refactor old pipeline code into modular SQL queries that could be more easily read, reused, and maintained. dbt Cloud made the data transformation process accessible to Vida Health employees across clinical research, product, and data teams, enabling them to share knowledge, add more precision to their data insights and product, and move faster. The company now uses built-in features from dbt Cloud and Fivetran to prevent issues that could create pipeline downtime and ensure a quick recovery when incidents occur. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Big Data Analytics

Functional Applications - Enterprise Resource Planning Systems (ERP)

Applicable Industries

Healthcare & Hospitals

Oil & Gas

Applicable Functions

Product Research & Development

Quality Assurance

Use Cases

Experimentation Automation

Leasing Finance Automation

Services

Cloud Planning, Design & Implementation Services

Testing & Certification

Challenge

Vida Health, a digital health company, was facing challenges with its data infrastructure. The company collects data on customers' medical history, past insurance claims, lab test results, and log data from health-tech devices to provide personalized virtual care. However, their custom-built solution using Python scripts and cron jobs to load and transform data in BigQuery was not scalable and often failed when data volume spiked. The pipeline was poorly documented and understood by only a few people on the data team, leading to reporting downtime of 2-3 days when issues arose. The company had recently consolidated its data engineering, data science, and data analytics functions into one team, aiming to improve collaboration. However, the existing data infrastructure was not reliable or accessible enough to best serve their customers and meet their goal of onboarding more than ten new clients in less than six months.

About Customer

Vida Health is a San Francisco-based digital health company founded in 2014. The company provides personalized virtual care to individuals with chronic conditions such as diabetes, obesity, and depression through employer-sponsored plans. To tailor support and resources to individual needs, Vida Health collects data on the customer's medical history, past insurance claims, lab test results, and log data from health-tech devices such as fitness trackers and digital scales. The data enables Vida Health to monitor user progress, adjust treatment plans as needed, and give users visibility into results via the Vida Health mobile app. The company has a team size of 400 and uses a data stack comprising dbt Cloud, Fivetran Enterprise, Google Cloud, BigQuery, and Looker.

Solution

Vida Health implemented Fivetran and dbt Cloud to transform their data infrastructure. Fivetran was used to centralize data from all their SaaS applications and proprietary Protected Health Information (PHI) from multiple sources into one source-of-truth warehouse. This provided built-in automation and security, especially crucial when handling health data. The company now uses Fivetran to centralize data from 148 different connectors into one BigQuery. Once the data was loaded into BigQuery with Fivetran, the team used dbt Cloud to refactor old pipeline code into modular SQL queries that could be more easily read, reused, and maintained. dbt Cloud made the data transformation process accessible to Vida Health employees across clinical research, product, and data teams, enabling them to share knowledge, add more precision to their data insights and product, and move faster. The company now uses built-in features from dbt Cloud and Fivetran to prevent issues that could create pipeline downtime and ensure a quick recovery when incidents occur.

Impact #1	The implementation of Fivetran and dbt Cloud has resulted in a more resilient and maintainable pipeline for Vida Health. The company can now easily pinpoint issues with data ingestion as they come up, thanks to having access to pipeline creation and monitoring all in one place with Fivetran. The fully managed connectors with schema migration and automation built in ensure data is always available. The team uses dbt Cloud's data lineage to trace bugs, snapshots to detect data changes over time, and built-in CI support to standardize testing and improve data quality. This has increased their velocity as they spend less time on maintenance. The modern data stack has provided Vida Health with the foundation needed to provide best-in-class care to their clients, driven by data.

Impact #1

The implementation of Fivetran and dbt Cloud has resulted in a more resilient and maintainable pipeline for Vida Health. The company can now easily pinpoint issues with data ingestion as they come up, thanks to having access to pipeline creation and monitoring all in one place with Fivetran. The fully managed connectors with schema migration and automation built in ensure data is always available. The team uses dbt Cloud's data lineage to trace bugs, snapshots to detect data changes over time, and built-in CI support to standardize testing and improve data quality. This has increased their velocity as they spend less time on maintenance. The modern data stack has provided Vida Health with the foundation needed to provide best-in-class care to their clients, driven by data.

Benefit #1	148 active connectors ingest data from various databases and SaaS data sources
Benefit #2	Reduced time to generate a KPI for executives from 3 months to 1 day
Benefit #3	80% of work to create new data products can now be self-served by the analytics team, up from 20%

Benefit #1

148 active connectors ingest data from various databases and SaaS data sources

Benefit #2

Reduced time to generate a KPI for executives from 3 months to 1 day

Benefit #3

80% of work to create new data products can now be self-served by the analytics team, up from 20%

Download PDF Version

Overview

Vida Health's Transformation: Personalized Healthcare through Modern Data Stack

Operational Impact

Quantitative Benefit